Assessing severe pneumonia risk in children via clinical prognostic model based on laboratory markers

Pneumonia represents a significant cause of mortality in children globally, emphasizing the importance of identifying high-risk patients to improve clinical outcomes. There is a lack of reliable laboratory markers and robust risk stratification models for clinical decision support in pediatric pneumonia. This study extracted data from the Paediatric Intensive Care database for 749 children under 3 years with severe pneumonia. The relationship between laboratory parameters and prognostic outcomes was evaluated using Cox proportional hazards regression analyses. Oxygen saturation, hemoglobin, lipase, urea, and uric acid were identified as laboratory parameters significantly associated with severe pneumonia outcomes. Leveraging these laboratory markers, a prognosis model was constructed employing the XGBoost classifier. The model was validated in a hold-out test cohort and an external validation cohort, with its performance assessed by the area under the receiver operating characteristic curve (AUC). The validation cohort was derived from 129 children with severe pneumonia admitted to the PICU of the Children's Hospital, Zhejiang University School of Medicine in 2019. The model demonstrated efficacy in predicting the death and survival of patients (AUC = 0.943), as well as in distinguishing between children at high- and low-risk of death in advance (HR = 2.930, 95 % CI: 2.551-3.366, P < 0.001). The robust performance of this model was further validated in the test cohort (AUC = 0.871), and the validation cohort (AUC = 0.872). In conclusion, this novel model enables the prediction of individualized mortality risk in children diagnosed with severe pneumonia, offering personalized risk assessments to inform and enhance clinical decision-making processes.